SPYtrading_main

import matplotlib
matplotlib.use('Agg')  # Set the backend to Agg for non-interactive plotting
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# List of S&P 500 component stocks (for demonstration purposes)
sp500_tickers = pd.read_html("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")
sp500_tickers = sp500_tickers[0]  #take just the first table from the webpage

# Initialize capital and equity curve list
initial_capital = 1_000_000
capital = initial_capital
equity_curve = []  # To store portfolio value over time

# Define trading parameters
rsi_buy_threshold = 35
rsi_sell_threshold = 55
rsi_short_threshold = 75
investment_percentage = 0.005  # Use 0.5% of capital


# Function to calculate RSI
def calculate_rsi(prices, period=14):
    delta = prices.diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()

    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))

    return rsi


# Iterate through each stock in the S&P 500
for ticker in sp500_tickers:
    print(f"Processing {ticker}...")

    # Fetch historical market data for each stock (5 years)
    stock_data = yf.Ticker(ticker).history(period="5y")

    # Calculate RSI and add it to the DataFrame
    stock_data['RSI'] = calculate_rsi(stock_data['Close'])

    position_size = 0

    for i in range(len(stock_data)):
        current_rsi = stock_data['RSI'].iloc[i]
        current_price = stock_data['Close'].iloc[i]

        # Buy condition (RSI <= 35)
        if current_rsi <= rsi_buy_threshold and position_size == 0:
            position_size = (capital * investment_percentage) / current_price
            capital -= position_size * current_price
            print(f"Buying {position_size:.2f} shares of {ticker} at {current_price:.2f}")

        # Sell condition (RSI >= 55) when holding a position
        elif current_rsi >= rsi_sell_threshold and position_size > 0:
            capital += position_size * current_price
            print(f"Selling {position_size:.2f} shares of {ticker} at {current_price:.2f}")
            position_size = 0

        # Short condition (RSI >= 75)
        elif current_rsi >= rsi_short_threshold and position_size == 0:
            position_size = -(capital * investment_percentage) / current_price
            capital += -position_size * current_price
            print(f"Shorting {abs(position_size):.2f} shares of {ticker} at {current_price:.2f}")

        # Cover condition (RSI <= 55) when holding a short position
        elif current_rsi <= rsi_sell_threshold and position_size < 0:
            capital += abs(position_size) * current_price
            print(f"Covers {abs(position_size):.2f} shares of {ticker} at {current_price:.2f}")
            position_size = 0

        # Append the current portfolio value to equity_curve list
        equity_curve.append(capital + (position_size * current_price if position_size != 0 else 0))

# Final capital after trading across all stocks
final_capital = capital + (position_size * stock_data['Close'].iloc[-1] if position_size != 0 else 0)
print(f"Final Capital after trading all stocks: ${final_capital:.2f}")

# Step for Visualization of the equity curve

# Create a plot for the equity curve over time
plt.figure(figsize=(14, 7))
plt.plot(equity_curve, label='Portfolio Equity Curve', color='blue')
plt.title('Portfolio Equity Curve Over Time')
plt.xlabel('Time (Days)')
plt.ylabel('Equity ($)')
plt.legend()
plt.grid()

# Save the plot as an image file instead of displaying it interactively.
plt.savefig("portfolio_equity_curve.png")